Tessmer Heidi L, Ito Kimihito, Omori Ryosuke
Division of Bioinformatics, Research Center for Zoonosis Control, Hokkaido University, Sapporo, Japan.
Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency, Saitama, Japan.
Front Microbiol. 2018 Mar 2;9:343. doi: 10.3389/fmicb.2018.00343. eCollection 2018.
To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, , is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly . In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1)pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.
为了估计和预测呼吸道病毒的传播动态，基本再生数(R_0)的估计至关重要。最近，近似贝叶斯计算方法已被用作无似然方法来估计流行病学模型参数，特别是(R_0)。在本文中，我们探索了各种机器学习方法，如多层感知器、卷积神经网络和长短期记忆网络，来学习和估计参数。此外，我们比较了机器学习方法和近似贝叶斯计算方法在甲型H1N1流感、腮腺炎和风疹爆发的模拟和实际流行病学数据上的估计准确性和时间要求。我们发现，机器学习方法比近似贝叶斯计算方法能更快地得到验证和测试，但近似贝叶斯计算方法在不同数据集上更稳健。
